Abstract
This paper highlights the relevance and merits of the Hartley modulating functions (HMF) method for the identification of bilinear continuous-time (BCT) systems from recorded input and noise-contaminated output data and it provides an insight into parameter estimation of a wider range of nonlinear systems in practice. The methodology replaces the I/O-differential equation representing the dynamic system behavior by the Hartley spectrum equation. As a result it involves the known derivatives of the modulating function instead of the derivatives of the input and noisy output data by applying integral transformation to signals. A frequency weighted least-squares algorithm is also applied in the identification and a normalized root mean square criterion is used to investigate some computational considerations and the bias of the estimates. Results of the simulation studies demonstrate the appropriateness of the approach and its efficiency.
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